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the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our
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research assistants, postdoctoral researchers, and academic staff to develop cutting-edge methodologies. The research is cross-disciplinary, combining advanced quantitative analysis, simulation, and systems
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DTU Biosustain you will break new ground at the absolute forefront of what is possible, focusing on expression of complex disulfide-containing proteins. In this position, you will develop tools
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machine learning for transport simulation. A core innovation involves Bayesian metamodeling techniques to construct fast surrogate models of the simulation space, enabling efficient scenario analysis
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use your skills to answer fundamental biological questions in health and disease. The successful candidate is expected to leverage their skills in proteomics and computational analysis to interrogate
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this role, you will help to interpret complex multimodal data, and you will do it in collaboration with top research scientist in the field and in an international, motivating, flexible, supportive, and
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on developing innovative freestanding membranes made from complex oxides. These membranes provide a unique approach to designing the next generation of complex oxide devices by utilizing novel stacking and